Instructions to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

Use Docker

docker model run hf.co/kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with Ollama:
```
ollama run hf.co/kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
```

Unsloth Studio

How to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF to start chatting

Docker Model Runner
How to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M
```

Lemonade

How to use kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF-Q5_K_M

List all available models

lemonade list

Atomic Chat

kkioikk/DeepSeek-V2-Lite-Chat-Q5_K_M-GGUF

This model was converted to GGUF format from deepseek-ai/DeepSeek-V2-Lite-Chat using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

小参数模型使用注意事项：

尽量使用简洁的系统指令，系统指令优先于即时输入指令。
当无法遵守指令时，可通过修改历史记录的方法，例如在开始时预设少量对话记录：{user},{assistant}，直接修改或替换模型输出作为历史记录，这样可以让模型更好地学习你的要求。

Precautions for using small-parameter models:

Try to use concise system instructions, system instructions take precedence over real-time input instructions.
When you can't follow the instructions, you can modify the history by modifying the history, such as presetting a small number of conversation records at the beginning: {user}, {assistant}, directly modify or replace the model output as the history, which can make the model learn your requirements better.

Downloads last month: 18

GGUF

Model size

16B params

Architecture

deepseek2

Hardware compatibility

5-bit

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